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《Acta Meteorologica Sinica》 2009-02
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Merging radar and rain gauge data using Kriging with external drift (KED) for quantitative precipitation estimation.

HUANG Xiaoyu1,2 CHEN Yuan1 XIONG Yi3 CHEN Bo4 XIA Zhenglong2 LIAO Yufang21.Hunan Provincial Key Laboratory of Meteorological Disaster Reduction,Changsha 410007,China2.Hunan Province Meteorological Office,Changsha 410007,China3.China Meteorological Administration,Beijing 100081,China4.National University of Defense Technology,Changsha 410001,China  
A new spatial information statistical method,Kriging with external drift(KED),which merges radar and rain gauge data to make quantitative precipitation estimation,is introduced and analyzed.The essential of merging radar and rain gauge data to estimate rainfall is to calibrate the radar with rain gauge data and syncretize the result of the rain gauge records into the radar detection while keeping the meso and small scale features of radar data.High accuracy,low tempo-spatial resolution rain gauge data is successfully combined with the low accuracy,high tempo-spatial resolution radar data using the KED interpolation.The covariance function is used to reflect the spatial variance structure,and the spatial continuity of data is fully considered.As the spatial structure of KED predicted rainfall field is obtained by using variogram,the estimation precision can be improved and the processing speed can be accelerated by making full use of the spatial relation among the data.The KED method is expected to advance operational quantitative precipitation estimation.Three methods,namely the radar-based precipitation estimation(RAD),the variation adjustment precipitation estimation(VAR),and the KED estimation,are compared with each other and verified against the rain gauge data for three representative rainfall cases in Hunan Province.The results show that the mean-square deviation,absolute error and relative error from RAD are bigger than those from VAR,and those of KED are the smallest.The results from KED agree well with the rain gauge data.Error frequency calculations for the three methods and the rain gauge data show that KED has the smallest average error and standard deviation,and the error distribution for KED is located near 0.Moreover,the skewness and kurtosis of KED are the best,while those of VAR take the second place and RAD performs the worst.The magnitude of the KED calibrated precipitation field is close to the rain gauge record,meanwhile the precipitation distribution pattern detected by radar was well retained.
【Fund】: 湖南省气象局重点项目(022 026 028)
【CateGory Index】: P412.13
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